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ENTITY Differential Refresh Policies for Models Trained on Lagging Data Snapshots: From a Single-Age Equivalence Limit to an Optimal Per-Segment Allocation

Differential Refresh Policies for Models Trained on Lagging Data Snapshots: From a Single-Age Equivalence Limit to an Optimal Per-Segment Allocation

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  1. TOOL · CL_286948 ·

    New research proposes differential refresh policies for ML models

    Researchers have developed a new approach to refreshing machine learning models trained on data that becomes outdated over time. They propose that instead of using a single, global staleness score to trigger retraining,…